Oliver Wyman – Associate Director - Data Science (AI and Generative AI) - Gurugram
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Oliver WymanDescription:
About Oliver Wyman
At Oliver Wyman, a Marsh (NYSE: MRSH) business, we bring deep industry insight, bold innovation, and a collaborative approach that cuts through complexity to help organizations navigate their most defining transformative moments.
As a business of Marsh, we work alongside the world’s leading experts across risk, reinsurance and capital, people and investments, and management consulting. Together with Marsh Risk, Guy Carpenter, and Mercer, we help organizations build resilience and competitive advantages from every angle. With annual revenue over $24 billion and more than 90,000 colleagues in 130 countries, Marsh helps build the confidence to thrive through the power of perspective.
For more information, visit oliverwyman.com, or follow us on LinkedIn and X
About Data and Analytics (DNA) Practice
At Oliver Wyman Data and Analytics, we partner with clients to solve tough strategic business challenges with the power of analytics, technology, and industry expertise. Our India DNA team brings high-quality analytics and quantitative talent into global consulting engagements, delivering practical, client-ready solutions across financial services and other priority sectors.
Role Summary
We are looking for a senior AI and Generative AI professional with strong technical depth, delivery leadership, and client-facing communication skills. The role will focus on leading AI / GenAI solution design, evaluation, governance, and implementation across client use cases such as knowledge assistants, document intelligence, workflow automation, decision support, advanced analytics, and responsible AI transformation.
You will work with Oliver Wyman partners, consultants, and senior client stakeholders to identify high-value AI opportunities, shape solution architecture, lead delivery workstreams, manage technical quality, and translate complex AI topics into practical business recommendations. This is a hands-on leadership role suited for someone who can balance technical depth, commercial judgment, and team development.
Key Responsibilities
Lead AI, machine learning, and GenAI workstreams from use-case discovery and solution design through prototyping, evaluation, deployment readiness, and adoption support.
Define technical scope, delivery approach, architecture options, workplans, quality standards, and success metrics for complex AI / GenAI engagements.
Partner with clients to identify high-value AI opportunities, assess feasibility, prioritize use cases, and translate business needs into data, model, platform, and governance requirements.
Lead development and review of GenAI applications such as RAG knowledge assistants, document intelligence solutions, enterprise copilots, workflow agents, semantic search, summarization / extraction tools, and prompt-driven analytics.
Advise on model and platform choices, including commercial LLMs, open-source models, small language models, vector databases, cloud AI services, integration patterns, and build-versus-buy considerations.
Oversee experimentation, prompt engineering, retrieval design, fine-tuning / adaptation approaches, benchmarking, model evaluation, performance monitoring, and issue remediation.
Establish responsible AI, LLMOps, MLOps, model governance, privacy, security, human-in-the-loop, auditability, and monitoring practices for AI-enabled solutions.
Review code, solution designs, documentation, demos, technical findings, and client deliverables to ensure analytical quality and client readiness.
Manage and coach junior team members, ensuring strong problem structuring, engineering discipline, documentation quality, and timely delivery.
o Partner with Oliver Wyman consultants and partners to shape proposals, client conversations, reusable assets, and thought leadership on AI and GenAI.
Required Experience and Qualifications
9 to 12 years of experience in AI / ML, data science, GenAI engineering, NLP, software engineering, data engineering, advanced analytics, AI strategy, or related consulting / technology roles.
Proven experience leading AI / GenAI or advanced analytics workstreams, including solution design, data assessment, model evaluation, technical delivery, documentation, and stakeholder management.
Bachelor's or master's degree in Computer Science, Data Science, Engineering, Statistics, Mathematics, Economics, AI / ML, or another quantitative or technical discipline; advanced degree preferred.
Strong technical knowledge of machine learning, NLP, LLMs, embeddings, RAG, agents, prompt engineering, fine-tuning, model evaluation, model monitoring, and AI application architecture.
Hands-on proficiency with Python and SQL; experience with cloud platforms, APIs, MLOps / LLMOps tools, orchestration frameworks, and large-scale data environments is an advantage.
Ability to assess model performance, solution reliability, data quality, hallucination risk, bias / fairness, explainability, limitations, controls, and business-use alignment.
Experience writing and reviewing senior-stakeholder-ready technical documentation, implementation plans, governance materials, and client deliverables.
Strong project management skills, including ability to manage multiple workstreams, deadlines, risks, stakeholders, and junior team members.
Excellent verbal and written communication skills, with the ability to translate complex AI / GenAI topics into practical business, risk, and technology implications.
Preferred / Valued Experience
Experience delivering enterprise AI / GenAI solutions in regulated or complex environments such as financial services, insurance, risk, finance, operations, customer service, or knowledge management.
Experience developing or reviewing responsible AI frameworks, AI governance policies, LLM evaluation standards, model risk controls, monitoring frameworks, and remediation plans.
Experience with Azure OpenAI, AWS Bedrock, Google Vertex AI, OpenAI-compatible APIs, Hugging Face, LangChain, LlamaIndex, vector databases, MLflow, Databricks, Snowflake, Spark, or Kubernetes.
Experience scaling AI prototypes into production-ready products, including integration design, security review, testing, change management, adoption tracking, and value measurement.
Consulting experience or experience in client-facing AI, analytics, product, technology, or transformation roles.
Experience shaping proposals, solution blueprints, accelerators, thought leadership, or commercial discussions around AI and GenAI.
What We Look For
Leadership presence with the ability to build trust with clients and internal teams.
Practical, impact-focused problem solving grounded in technical depth.
Strong coaching mindset and commitment to developing India-based analytics and AI talent.
Ability to balance hands-on delivery with architecture, governance, and commercial context.
Curiosity about emerging AI techniques with sound judgment about risk, quality, and adoption.
Comfort working with global teams across time zones and traveling when required.
How we rate this
Oliver Wyman – Associate Director - Data Science (AI and Generative AI) - Gurugram at Marsh McLennan rates 70 out of 100 for how much of the daily work is AI. That makes it Works on AI (AI Level 3 of 4). The level is about AI in the job, not seniority.
Works on AI. The daily work is on AI products, without building the model.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.
Prepare for this job
A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.
Skills and AI tools this role asks for
Questions you could be asked
- How do you structure and test a prompt to get consistent output from a language model?
- How would you design a retrieval step so the model answers from real data instead of guessing?
- Walk me through fine-tuning a model: what data did you use, and how did you check the result?
- How do you monitor a model once it's live, and how do you know it needs retraining?
- How do you decide that one model's output is better than another's for a given task?
Adapt your resume
- List these exact terms on your resume: Prompt Engineering, RAG, Fine Tuning, ML Ops, and AI Evaluation. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
- Show where AI is part of your daily process, not a one-off project — this role expects it to be a running habit.
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